Al-Musannah vs Saham: result 1-3 and analysis
Professional LeagueRegular Season - 21Kick-off on March 4, 2016 at 01:30 PM (UTC)Al-Seeb Stadium, Seeb
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What was the result of Al-Musannah vs Saham?
Saham won 1-3 on March 4, 2016.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Al-Musannah have 4 wins, 3 draws and 8 defeats; Saham have 5, 6 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Feb 19, 2016 | Al Oruba(home) | 0-1 | Professional League |
| Feb 11, 2016 | Dhofar(away) | 0-1 | Professional League |
| Feb 4, 2016 | Sur(home) | 1-1 | Professional League |
| Jan 31, 2016 | Salalah(away) | 0-2 | Professional League |
| Jan 26, 2016 | Sohar(home) | 2-1 | Professional League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Feb 20, 2016 | Dhofar(home) | 1-0 | Professional League |
| Feb 11, 2016 | Sur(away) | 2-2 | Professional League |
| Feb 3, 2016 | Salalah(home) | 3-1 | Professional League |
| Jan 30, 2016 | Sohar(away) | 0-0 | Professional League |
| Jan 27, 2016 | Al-Shabab(home) | 1-1 | Professional League |
What does the head-to-head say?
In the last meeting: 0 Al-Musannah wins, 0 draws, 1 Saham win.
| Date | Score | Competition |
|---|---|---|
| Nov 28, 2015 | Saham 1-0 Al-Musannah | Professional League |
How many goals should we expect?
Al-Musannah score 0.7 and concede 0.9 goals per match over their last 15; Saham score 1.1 and concede 1.0.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Al-Musannah | 0.7 | 0.9 | 3 | 5/15 | 3/15 |
| Saham | 1.1 | 1.0 | 6 | 6/15 | 4/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 5 | Saham | 6 | 3 | 0 | 3 | 5 | 6 | 9 |
A question about Al-Musannah vs Saham?
Ask Footlab AI: lineups, absences, player form, stats, how the match could unfold.
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How does Footlab AI compute this prediction?
The model combines both teams' Elo ratings (anchored by competition pool), their recent form weighted by opponent strength, home advantage and a Poisson matrix for scorelines. When bookmakers publish odds for the match, their consensus is built into the calculation: that is what proved most accurate on past matches. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy